The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Investigating the Acceptance of Mobile Library Applications with an Extended Technology Acceptance Model (TAM)
Open accessAbstract
Abstract Mobile applications are getting a great deal of interest among researchers due to their proliferation and pervasiveness, especially in the context of digital libraries of educational institutes. However, their low acceptance and usage are observed, hence, in-depth investigations are required in order to understand the factors behind low acceptance and intention to use mobile library application (MLA). Therefore, the aim of this work is to empirically explore the acceptance of MLA with a proposed model that is evolved from the technology acceptance model (TAM). The study objects to deliver empirical provision on acceptance of MLA. A self-administrated cross-sectional survey-based study was conducted to gather data from 340 users of MLA. Structural equation model (SEM) with an analysis of moment structure (AMOS) software was conducted to examine quantitative data. Results revealed that perceived usefulness and perceived ease of use are direct significant predictors with the intention to use MLA whereas system quality and habit are the influencing factors toward the usage intention of MLA. The findings help as a guide for effective decision in the design and development of MLA. Further, the outcomes can be utilized in the resource allocation process for ensuring the success of the library's vision and mission.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Evaluating Structural Equation Models with Unobservable Variables and Measurement Error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.2307/3151312 · Cited by 109,314
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
User Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012) · MIS Quarterly · doi:10.2307/41410412 · Cited by 15,756
Attitude-behavior relations: A theoretical analysis and review of empirical research
Ajzen, I., & Fishbein, M. (1977) · Psychological Bulletin · doi:10.1037/0033-2909.84.5.888 · Cited by 7,611 · cited 1×
“TAM was extended from the theory of reasoned action (TRA), which says, the behaviour of an individual is determined by his/her behavioural intention (BI) whereas, BI is determined by subjective norms and attitude (Ajzen & Fishbein, 1977).”
BackgroundEmpirical Evaluation of the Revised Technology Acceptance Model
Szajna, B. (1996) · Management Science · doi:10.1287/mnsc.42.1.85 · Cited by 1,656
Extending the technology acceptance model: the influence of perceived user resources
Mathieson, K., Peacock, E., & Chin, W. W. (2001) · ACM SIGMIS Database: the DATABASE for Advances in Information Systems · doi:10.1145/506724.506730 · Cited by 1,099
Cited by
Choi, S., Jang, Y., & Kim, H. (2023) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2022.2049145 · Cited by 294
Dahri, N. A., Yahaya, N., Al-Rahmi, W. M., Aldraiweesh, A., Alturki, U., Almutairy, S., Shutaleva, A., & Soomro, R. B. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e29317 · Cited by 239
“…employed to understand the determinants of technology acceptance in various regions including Germany [50], US [51], Saudi Arabia [52], Pakistan [53], Malaysia [54], Turkey [55], Greece [56], Indonesia [57], South Korea [58], and China [56,57], and across different technology-related domains…”
BackgroundChai, C. S., Wang, X., & Xu, C. (2020) · Mathematics · doi:10.3390/math8112089 · Cited by 226
Zhang, Z., Cao, T., Shu, J., & Liu, H. (2022) · Interactive Learning Environments · doi:10.1080/10494820.2020.1723113 · Cited by 164
Najib, M., & Fahma, F. (2020) · International Journal on Advanced Science, Engineering and Information Technology · doi:10.18517/ijaseit.10.4.11616 · Cited by 161
Metadata
- Authors
- Rafique, H., Almagrabi, A. O., Shamim, A., Anwar, F., & Bashir, A. K.
- Venue
- Computers & Education
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 145
- Pages
- 103732
- ISSN
- 0360-1315 (ISSN-L)
- OpenAlex
- W2981252154
- Cited by
- 486
- References
- 7
- FWCI
- 46.89
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.